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https://issues.apache.org/jira/browse/SPARK-6332?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-6332:
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Assignee: (was: Apache Spark)
> compute calibration curve for binary classifiers
> ------------------------------------------------
>
> Key: SPARK-6332
> URL: https://issues.apache.org/jira/browse/SPARK-6332
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Robert Dodier
> Priority: Minor
> Labels: classification
>
> For binary classifiers, calibration measures how classifier scores compare to
> the proportion of positive examples. If the classifier is well-calibrated,
> the classifier score is approximately equal to the proportion of positive
> examples. This is important if the scores are used as probabilities for
> making decisions via expected cost. Otherwise, the calibration curve may
> still be interesting; the proportion of positive examples should at least be
> a monotonic function of the score.
> I propose that a new method for calibration be added to the class
> BinaryClassificationMetrics, since calibration seems to fit in with the ROC
> curve and other classifier assessments.
> For more about calibration, see:
> http://en.wikipedia.org/wiki/Calibration_%28statistics%29#In_classification
> References:
> Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht. "Binary
> Classifier Calibration: Non-parametric approach."
> http://arxiv.org/abs/1401.3390
> Alexandru Niculescu-Mizil, Rich Caruana. "Predicting Good Probabilities With
> Supervised Learning." Appearing in Proceedings of the 22nd International
> Conference on Machine Learning, Bonn, Germany, 2005.
> http://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf
> "Properties and benefits of calibrated classifiers." Ira Cohen, Moises
> Goldszmidt. http://www.hpl.hp.com/techreports/2004/HPL-2004-22R1.pdf
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